> Markdown version of [/jobs/ext/3050841-executive-director-data-management-architecture-employee-platforms](https://www.wearedevelopers.com/jobs/ext/3050841-executive-director-data-management-architecture-employee-platforms). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Executive Director Data Management Architecture - Employee platforms

- **Company:** JPMorgan Chase & Co.
- **Location:** Jersey City, NJ, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Component-Based Software Engineering, Application Frameworks, Big Data, Data Architecture, Data Governance, Extract Transform Load (ETL), Fault Tolerance, Metadata Standards, Data Streaming, Management of Software Versions, Data Ingestion, Change Data Capture, Event Driven Architecture, Data Management, Data Pipelines
- **Published:** September 24, 2026
- **Apply:** https://www.jobmonkeyjobs.com/career/28046996/Executive-Director-Data-Management-Architecture-Employee-Platforms-New-Jersey-Jersey-City-7463

## About the Role

* Formal training or certification on data architecture concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
 * Significant experience designing and delivering large-scale data platforms, including ingestion, transformation and refinement, and access and serving layers
 * Demonstrated experience supporting transactional systems and real-time streaming and event-driven architectures, including patterns for reliability, consistency, and scalability
 * Proven ability to design platforms that are extensible, modular, and evolvable - including component-based architecture, reusable frameworks, well-defined interfaces and contracts, and backward-compatible change patterns
 * Strong experience with data modeling (conceptual, logical, and physical), schema and versioning strategy, and designing for interoperability across domains
 * Experience implementing architecture that supports data governance in a multi-domain enterprise - including metadata, lineage, ownership and stewardship alignment, change control, and quality management
 * Strong stakeholder management skills with the ability to translate business needs into technical architecture and platform roadmaps
 * Strong written and verbal communication skills, with the ability to present complex architectural concepts clearly to both technical and non-technical audiences

Preferred Qualifications, Capabilities, and Skills

 * Experience with human resources or employee data domains, or internal enterprise platforms managing sensitive employee information
 * Experience with streaming architectures and event-driven integration patterns at enterprise scale
 * Experience with platform observability, data quality tooling, and operational support models for data pipelines
 * Familiarity with privacy-by-design principles and risk-aware delivery practices in regulated or enterprise environments

## Description

* Define target-state architecture for employee data ingestion, refinement, storage, and access - ensuring scale, reliability, performance, and long-term maintainability across domains and platforms
 * Establish architectural principles and reference patterns that promote modularity, loose coupling, reusability, and evolvability, enabling the platform to adapt as business requirements and technology capabilities change
 * Design ingestion and integration patterns for multiple upstream employee platform systems, including batch and streaming approaches, schema evolution, resilient integration, and fault tolerance
 * Architect refinement pipelines that produce trusted, reusable curated datasets and standardized entities and events to support a broad range of downstream use cases
 * Design and support architectures that integrate transactional systems, batch pipelines, and real-time event streaming - including patterns for low-latency data movement, change data capture, and idempotent processing
 * Lead conceptual, logical, and physical data modeling for key employee data domains; drive consistent definitions, metadata standards, and documentation to enable discoverability and reuse
 * Define architectural controls for data quality, lineage, platform monitoring, and operational support - including service-level objectives, incident patterns, and runbooks
 * Partner with data product, governance, and control stakeholders to implement architecture that supports ownership, definition consistency, controlled change management, and quality management across domains
 * Ensure platform designs support responsible handling of personal information and align to firm data risk expectations through close partnership with appropriate stakeholders
 * Lead and mentor data architects and engineers, providing design reviews, reference implementations, and hands-on guidance; collaborate with internal stakeholders on roadmap planning and delivery execution


## Related Videos

- [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology)
- [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering)
- [Python-Based Data Streaming Pipelines Within Minutes](https://www.wearedevelopers.com/videos/1233-python-based-data-streaming-pipelines-within-minutes)
- [When React Meets Reality: Building a Real-Time Control Room for Autonomous Vehicles](https://www.wearedevelopers.com/videos/2091-when-react-meets-reality-building-a-real-time-control-room-for-autonomous-vehicles)
- [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions)
- [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data)

## Related Articles

- [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [Why Event-Driven Architecture Isn’t About Speed (and When You Actually Need It)](https://www.wearedevelopers.com/magazine/745-why-event-driven-architecture-isn-t-about-speed-and-when-you-actually-need-it)
- [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)
- [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)
- [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)